Pairing Underwriters With Machines

Risk assessment has always been one of the most challenging and time-consuming pieces of the underwriting process, but with the help of machine learning, natural language understanding (NLU) and other techniques, underwriting is becoming more accurate and efficient.

It’s important to note that when we talk about machine learning and artificial intelligence, we’re not talking about replacing underwriters with algorithms. Instead, by pairing underwriters with machine learning tools that analyze mass quantities of data, we can create much more accurate risk evaluations. This allows underwriters to work at a higher level, use more data to make accurate assessments, and become better at their jobs.

Machine learning streamlines and speeds up the risk evaluation process by making more accurate inferences and projections from large volumes of data. Algorithms filtered through unbiased machines can be great tools during the risk evaluation process. In our casualty business, for instance, preliminary analysis found that algorithms could handle more than a third of the book – namely, the transactional part – without human interaction, freeing up time for employees to work on other important tasks such as diving into highly complex risks and deepening producer relationships.

Better Determining Workplace Risks

Let’s consider this example: When regional plumbing companies are looking for commercial coverage, their level of exposure from an insurance standpoint depends on whether they perform residential, commercial or municipal plumbing. Breaking a pipe in a house is much different than causing an industrial sewer spill.

When asked, plumbers may classify themselves as residential because that drives the majority of their business, but they may fail to mention that they do occasional commercial or municipal jobs. This is only discovered when a claim is filed, resulting in a shortfall in coverage. By using NLU algorithms, underwriters can leverage public, private and governmental information to automatically assess if the plumber actually performs only residential work or does commercial work as well based on how they classify and talk about their business.

Machine learning offers businesses the chance to encode the best practices of their underwriters, allowing them to assess more risks.

Bolstering Staff Expertise

Unbiased machines also offer benefits to streamline very complicated work. Take underwriting mines as an example. To underwrite a mine, you need mine engineering expertise – a unique skillset. Even if a business wanted to underwrite 10 times the mining business it currently has, it might not be able to find enough underwriters with the necessary skillset.

However, machine learning offers businesses the chance to encode the best practices of their underwriters, allowing them to assess more risks. Each underwriter performs differently, with a list of preferred resources and special skills. That means an insurer can take certain aspects of what its underwriters do, build those aspects into a machine learning algorithm and scale them to create an effective model. Although algorithms and artificial intelligence do not replace human workers, they make employees much more efficient and less error-prone, especially when there’s a huge number of specialty risks that need to be assessed.

People vs. Computer Skills

Traditional actuarial models have always been about pattern matching; machine learning simply augments those existing tools to create more accurate underwriting risk evaluations. Humans are good at negotiating, sales and decision-making. Machine learning and algorithms are good at taking huge quantities of data, analyzing that data to make inferences and detecting patterns.

What machine learning does is let you apply data to more complex problems. A traditional auto insurance model might have five variables, for instance. But in specialty insurance, there may be hundreds or thousands of variables involved, which is where machine learning offers great benefits.

Techniques such as NLU also widely expand the sort of datasets available to underwriters. For example, NLU lets you search through textual information to automatically find key concepts and phrases, which can then be brought to a claims adjuster’s attention.

Combined with data mining and text analysis techniques, NLU can:

Streamline the flow of data to the correct departments or agents.

Improve an insurer’s decision-making by providing timely and accurate data.

Boost service-level agreement response times.

Help detect problematic claims and activity.

Using Quality Data Is Essential

Accurate risk evaluations require good data. Without using quality data in training algorithms, you will not get good outputs. An important step is to test your models against known outcomes, so you can see the quality before deploying a forward-looking predictor.

This also requires large volumes of data to train your algorithms and create more accurate risk evaluations. While there are efforts underway to use “small data” to train models, it’s still in the early days. Machine learning requires large amounts of cleaned, validated data; this data can either be imported from your organization’s existing databases or acquired from a third party.

Although the data revolution is transforming the insurance industry, we’re still a long way from artificial general intelligence* or any sort of science fiction-type singularity scenario. But in terms of a marketplace where machine learning is transforming the way business is processed, we are already there.

*Editor’s Note: Wikipedia defines “artificial general intelligence” as the intelligence of a machine that could successfully perform any intellectual task that a human being can.

In a report titled, “The State of Artificial Intelligence,” CB Insights provides the following definition: “Artificial General Intelligence or General AI is the concept of an AI system with human-level intelligence and cognitive ability that can perform a broad range of tasks and apply that knowledge to solve unfamiliar problems without being trained to do so.” Emphasis added.